ABSTRACT
As societies around the world are ageing, the number of Alzheimer’s disease (AD) patients is rapidly increasing. To date, no low-cost, non-invasive biomarkers have been established to advance the objectivization of AD diagnosis and progression assessment. Here, we utilize Bayesian neural networks to develop a multivariate predictor for AD severity using a wide range of quantitative EEG (QEEG) markers. The Bayesian treatment of neural networks both automatically controls model complexity and provides a predictive distribution over the target function, giving uncertainty bounds for our regression task. It is therefore well suited to clinical neuroscience, where data sets are typically sparse and practitioners require a precise assessment of the predictive uncertainty. We use data of one of the largest prospective AD EEG trials ever conducted to demonstrate the potential of Bayesian deep learning in this domain, while comparing two distinct Bayesian neural network approaches, i.e., Monte Carlo dropout and Hamiltonian Monte Carlo.

BACKGROUND:
So far, no cost-efficient, widely-used biomarkers have been established to facilitate the objectivization of Alzheimer’s disease (AD) diagnosis and monitoring. Research suggests that event-related potentials (ERPs) reflect neurodegenerative processes in AD and might qualify as neurophysiological AD markers.

OBJECTIVES:
First, to examine which ERP component correlates the most with AD severity, as measured by the Mini-Mental State Examination (MMSE). Then, to analyze the temporal change of this component as AD progresses.

METHODS:
Sixty-three subjects (31 with possible, 32 with probable AD diagnosis) were recruited as part of the cohort study Prospective Dementia Registry Austria (PRODEM). For a maximum of 18 months patients revisited every 6 months for follow-up assessments. ERPs were elicited using an auditory oddball paradigm. P300 and N200 latency was determined with regard to target as well as difference wave ERPs, whereas P50 amplitude was measured from standard stimuli waveforms.

RESULTS: P300 latency exhibited the strongest association with AD severity (e.g., r=–0.512, p<0.01 at Pz for target stimuli in probable AD subjects). Further, there were significant Pearson correlations for N200 latency (e.g., r=–0.407, p=0.026 at Cz for difference waves in probable AD subjects). P50 amplitude, as measured by different detection methods and at various scalp sites, did not significantly correlate with disease severity neither in probable AD, possible AD, nor in both subgroups of patients combined. ERP markers for the group of possible AD patients did not show any significant correlations with MMSE scores. Post-hoc pairwise comparisons between baseline and 18-months follow-up assessment revealed significant P300 latency differences (e.g., p<0.001 at Cz for difference waves in probable AD subjects). However, there were no significant correlations between the change rates of P300 latency and MMSE score.

CONCLUSIONS:
P300 and N200 latency significantly correlated with disease severity in probable AD, whereas P50 amplitude did not. P300 latency, which showed the highest correlation coefficients with MMSE, significantly increased over the course of the 18 months study period in probable AD patients. The magnitude of the observed prolongation is in line with other longitudinal AD studies and substantially higher than in normal ageing, as reported in previous trials (no healthy controls were included in our study).

AFFILIATION
(1) Medical University of Vienna, Section for AI and Decision Support
(2) University of Oxford, Department of Engineering Science, Machine Learning Research Group
(3) University of Oxford, Department of Computer Science
(4) Dr. Grossegger & Drbal GmbH
(5) Medical University of Graz, Department of Neurology
(6) Medical University of Innsbruck, Department of Neurology
(7) Medical University of Vienna, Department of Neurology
(8) Linz General Hospital, Department of Neurology
(9) AIT Austrian Institute of Technology GmbH

ABSTRACT
Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPA-GPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study, we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks.

AFFILIATION
(1) Medical University of Vienna
(2) University of Oxford
(3) Technical University of Denmark
(4) Medical University of Graz
(5) Medical University of Innsbruck
(6) Linz General Hospital
(7) Dr. Grossegger & Drbal GmbH
(8) AIT Austrian Institute of Technology GmbH
(9) University College London

ABSTRACT
The diagnosis of Alzheimer’s disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Quantitative electroencephalography (QEEG) measures have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel framework combining Riemannian tangent space mapping and elastic net regression for the development of brain atrophy markers. While most AD QEEG studies are based on small sample sizes and psychological test scores as outcome measures, here we train and test our models using data of one of the largest prospective EEG AD trials ever conducted, including MRI biomarkers of brain atrophy.

AFFILIATION
(1) Medical University of Vienna, Section for AI and Decision Support
(2) University of Oxford, Department of Engineering Science, Machine Learning Research Group
(3) University of Oxford, Department of Computer Science
(4) Dr. Grossegger & Drbal GmbH
(5) Medical University of Graz, Department of Neurology
(6) Medical University of Innsbruck, Department of Neurology
(7) Medical University of Vienna, Department of Neurology
(8) Linz General Hospital, Department of Neurology
(9) AIT Austrian Institute of Technology GmbH

ABSTRACT
The diagnosis of Alzheimer’s disease (AD) in routine clinical practice is most commonly based on subjective clinical interpretations. Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in AD and might qualify as affordable and thereby widely available markers to facilitate the objectivization of AD assessment. Here, we present a novel method com- bining multivariate pattern analysis (MVPA) and Gaussian process classification (GP) and aim to develop ERP markers for two crucial AD classifica- tion problems, i.e., the prediction of rapid cognitive decline (RCD) and the distinction between carriers and non-carriers of the ε4 allele of the apolipopro- tein E gene, the main genetic risk factor for AD.

ABSTRACT
We analyzed the relation of several synchrony markers in the electroencephalogram (EEG) and Alzheimer’s disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores. The study sample consisted of 79 subjects diagnosed with probable AD. All subjects were participants in the PRODEM-Austria study. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. We employed quadratic least squares regression to describe the relation between MMSE and the EEG markers. Factor analysis was used for estimating a potentially lower number of unobserved synchrony factors. These common factors were then related to MMSE scores as well. Most markers displayed an initial increase of EEG synchrony with MMSE scores from 26 to 21 or 20, and a decrease below. This effect was most prominent during the cognitive task and may be owed to cerebral compensatory mechanisms. Factor analysis provided interesting insights in the synchrony structures and the first common factors were related to MMSE scores with coefficients of determination up to 0.433. We conclude that several of the proposed EEG markers are related to AD severity for the overall sample with a wide dispersion for individual subjects. Part of these fluctuations may be owed to fluctuations and day-to-day variability associated with MMSE measurements. Our study provides a systematic analysis of EEG synchrony based on a large and homogeneous sample. The results indicate that the individual markers capture different aspects of EEG synchrony and may reflect cerebral compensatory mechanisms in the early stages of AD.

HIGHLIGHTS
– Largest clinical study of quantitative EEG markers for slowing, synchrony and complexity severity including 118 patients.
– Advanced metrics for quantitative EEG in resting state and during a face–name encoding
– MMSE scores explaining up to 51% of the variations in QEEG markers.

OBJECTIVE
To investigate which single quantitative electro-encephalographic (QEEG) marker or which combination of markers correlates best with Alzheimer’s disease (AD) severity as measured by the Mini-Mental State Examination (MMSE).

RESULTS
For the whole group (39 possible, 79 probable AD cases) MMSE scores explained 33% of the variations in relative theta power during face encoding, and 31% of auto-mutual information in resting state with eyes closed. MMSE scores explained also 25% of the overall QEEG factor. This factor was thus subordinate to individual markers. In probable AD, QEEG coefficients of determination were always higher than in the whole group, where MMSE scores explained 51% of the variations in relative theta power.

CONCLUSIONS
Selected QEEG markers show strong associations with AD severity. Both cognitive and resting state should be used for QEEG assessments.

SIGNIFICANCE
Our data indicate theta power measured during face-name encoding to be most closely related to AD severity.

BACKGROUND
Quantitative electroencephalogram (qEEG) recorded during cognitive tasks has been shown to differentiate between patients with Alzheimer’s disease (AD) and healthy individuals. However, the association between various qEEG markers recorded during mnestic paradigms and clinical measures of AD has not been studied in detail.

OBJECTIVE
To evaluate if ‘cognitive’ qEEG is a useful diagnostic option, particularly if memory paradigms are used as cognitive stimulators.

METHODS
This study is part of the Prospective Registry on Dementia in Austria (PRODEM), a multicenter dementia research project. A cohort of 79 probable AD patients was included in a cross-sectional analysis. qEEG recordings performed in resting states were compared with recordings during cognitively active states. Cognition was evoked with a face–name paradigm and a paired-associate word list task, respectively. Relative band powers, coherence and auto-mutual information were computed as functions of MMSE scores for the memory paradigms and during rest. Analyses were adjusted for the co-variables age, sex, duration of dementia and educational level.

RESULTS
MMSE scores explained 36–51% of the variances of qEEG-markers. Face–name encoding with eyes open was superior to resting state with eyes closed in relative theta and beta1 power as well as coherence, whereas relative alpha power and auto-mutual information yielded more significant results during resting state with eyes closed.

CONCLUSION
qEEG alterations recorded during mnestic activity, particularly face–name encoding showed the highest association with the MMSE and may serve as a clinically valuable marker for disease severity.

ABSTRACT
We investigated the correlation of Alzheimer’s disease (AD) severity as measured by the Mini-Mental State Examination (MMSE) to the signal complexity measures auto-mutual information, Shannon entropy and Tsallis entropy in 79 patients with probable AD from the multi-centric Prospective Dementia Database Austria (PRODEM). Using quadratic (linear) regressions, auto-mutual information explained up to 48% (43%), Shannon entropy up to 48% (37%) and Tsallis entropy up to 49% (35%) of the variations in MMSE scores, all at left temporal (T7) electrode site. The steepest slope of the linear regression was found for auto-mutual information (Δy/Δx = 36). For Shannon and Tsallis entropy, slopes were less steep. Comparing to traditional slowing measures, complexity measures yielded higher coefficients of determination. We conclude that auto-mutual information is well suited to characterize disease severity in mild to moderate AD.

ABSTRACT
We analyzed the relation between Alzheimer’s disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores and quantitative electroencephalographic (qEEG) markers that were derived from canonical correlation analysis. This allowed an investigation of EEG synchrony between groups of EEG channels. In this study, we applied the data from 79 participants in the multi-centric cohort study PRODEM-Austria with probable AD. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. A quadratic regression model was used to describe the relation between MMSE and the qEEG synchrony markers. This relation was most significant in the δ and θ frequency bands in resting state, and between left-hemispheric central, temporal and parietal channel groups during the cognitive task. Here, the MMSE explained up to 40% of the qEEG marker’s variation. QEEG markers showed an ambiguous trend, i.e. an increase of EEG synchrony in the initial stage of AD (MMSE>20) and a decrease in later stages. This effect could be caused by compensatory brain mechanisms. We conclude that the proposed qEEG markers are closely related to AD severity. Despite the ambiguous trend and the resulting diagnostic ambiguity, the qEEG markers could provide aid in the diagnostics of early-stage AD.